scholarly journals Towards Reliable Probabilistic Time-Series Projections of Global Mean Surface Temperature

2021 ◽  
Author(s):  
Philip G. Sansom ◽  
Donald Cummins ◽  
Stefan Siegert ◽  
David B Stephenson
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Surface Temperature ◽  
Climate Model ◽  
Future Projections ◽  
Perfect Model ◽  
Global Mean Surface Temperature ◽  
Time Series Approach ◽  
Energy Balance Models ◽  
Global Mean

Abstract Quantifying the risk of global warming exceeding critical targets such as 2.0 ◦ C requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. This approach gives reliable projections of GMST when tested in a perfect model setting. When applied to observations this leads to projected warming of 2.2 ◦ C (1.7 ◦ C to 2.9 ◦ C) in 2100 compared to pre-industrial conditions, 0.4 ◦ C lower than a comparable IPCC anomaly estimate. The probability of staying below the critical 2.0 ◦ C warming target in 2100 more than doubles to 0.28 compared to only 0.11 from a comparably IPCC estimate.

scholarly journals Identifying Signatures of Natural Climate Variability in Time Series of Global-Mean Surface Temperature: Methodology and Insights

2009 ◽  
Vol 22 (22) ◽  
pp. 6120-6141 ◽  
Author(s):  
David W. J. Thompson ◽  
John M. Wallace ◽  
Phil D. Jones ◽  
John J. Kennedy
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Surface Temperature ◽  
High Latitude ◽  
Volcanic Eruptions ◽  
Natural Variability ◽  
Natural Climate Variability ◽  
Global Mean Surface Temperature ◽  
Natural Climate ◽  
Global Mean

Abstract Global-mean surface temperature is affected by both natural variability and anthropogenic forcing. This study is concerned with identifying and removing from global-mean temperatures the signatures of natural climate variability over the period January 1900–March 2009. A series of simple, physically based methodologies are developed and applied to isolate the climate impacts of three known sources of natural variability: the El Niño–Southern Oscillation (ENSO), variations in the advection of marine air masses over the high-latitude continents during winter, and aerosols injected into the stratosphere by explosive volcanic eruptions. After the effects of ENSO and high-latitude temperature advection are removed from the global-mean temperature record, the signatures of volcanic eruptions and changes in instrumentation become more clearly apparent. After the volcanic eruptions are subsequently filtered from the record, the residual time series reveals a nearly monotonic global warming pattern since ∼1950. The results also reveal coupling between the land and ocean areas on the interannual time scale that transcends the effects of ENSO and volcanic eruptions. Globally averaged land and ocean temperatures are most strongly correlated when ocean leads land by ∼2–3 months. These coupled fluctuations exhibit a complicated spatial signature with largest-amplitude sea surface temperature perturbations over the Atlantic Ocean.

scholarly journals Time-Varying Response of ENSO-Induced Tropical Pacific Rainfall to Global Warming in CMIP5 Models. Part II: Intermodel Uncertainty

2017 ◽  
Vol 30 (2) ◽  
pp. 595-608 ◽  
Author(s):  
Ping Huang
Keyword(s):  
Global Warming ◽  
Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Tropical Pacific ◽  
Cmip5 Models ◽  
Central Pacific ◽  
Global Mean Surface Temperature ◽  
Global Mean ◽  
Mean State

Anomalous rainfall in the tropical Pacific driven by El Niño–Southern Oscillation (ENSO) is a crucial pathway of ENSO’s global impacts. The changes in ENSO rainfall under global warming vary among the models, even though previous studies have shown that many models project that ENSO rainfall will likely intensify and shift eastward in response to global warming. The present study evaluates the robustness of the changes in ENSO rainfall in 32 CMIP5 models forced under the representative concentration pathway 8.5 (RCP8.5) scenario. The robust increase in mean-state moisture dominates the robust intensification of ENSO rainfall. The uncertain amplitude changes in ENSO-related SST variability are the largest source of the uncertainty in ENSO rainfall changes through influencing the amplitude changes in ENSO-driven circulation variability, whereas the structural changes in ENSO SST and ENSO circulation enhancement in the central Pacific are more robust than the amplitude changes. The spatial pattern of the mean-state SST changes—the departure of local SST changes from the tropical mean—with an El Niño–like pattern is a relatively robust factor, although it also contains pronounced intermodel differences. The intermodel spread of historical ENSO circulation is another noteworthy source of the uncertainty in ENSO rainfall changes. The intermodel standard deviation of ENSO rainfall changes increases along with the increase in global-mean surface temperature. However, the robustness of enhanced ENSO rainfall changes in the central-eastern Pacific is almost unchanged, whereas the eastward shift of ENSO rainfall is increasingly robust along with the increase in global-mean surface temperature.

A Quantitative Definition of Global Warming Hiatus and 50-Year Prediction of Global-Mean Surface Temperature*

2015 ◽  
Vol 72 (8) ◽  
pp. 3281-3289 ◽  
Author(s):  
Meng Wei ◽  
Fangli Qiao ◽  
Jia Deng
Keyword(s):  
Global Warming ◽  
Surface Temperature ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Global Mean Surface Temperature ◽  
Global Warming Hiatus ◽  
Empirical Mode Decomposition Method ◽  
Warming Hiatus ◽  
Quantitative Definition ◽  
Global Mean

Abstract Recent global warming hiatus has received much attention; however, a robust and quantitative definition for the hiatus is still lacking. Recent studies by Scafetta, Wu et al., and Tung and Zhou showed that multidecadal variability (MDV) is responsible for the multidecadal accelerated warming and hiatuses in historical global-mean surface temperature (GMST) records, though MDV itself has not received sufficient attention thus far. Here, the authors introduce four key episodes in GMST evolution, according to different phases of the MDV extracted by the ensemble empirical-mode decomposition method from the ensemble HadCRUT4 monthly GMST time series. The “warming (cooling) hiatus” and “typical warming (cooling)” periods are defined as the 95% confidence intervals for the locations of local MDV maxima (minima) and of their derivatives, respectively. Since 1850, the warming hiatuses, cooling hiatuses, and typical warming have already occurred three times and the typical cooling has occurred twice. At present, the MDV is in its third warming-hiatus period, which started in 2012 and would last until 2017, followed by a 30-yr cooling episode, while the trend will sustain the current steady growth in the next 50 years. Their superposition presents steplike rising since 1850. It is currently ascending a new height and will stay there until the next warming phase of the MDV carries it higher.

Simulation and Projection of Global Mean Surface Temperature Using the Empirical Model of Global Climate and Comparison to CMIP6 GCMs

2021 ◽  
Author(s):  
Laura McBride ◽  
Austin Hope ◽  
Timothy Canty ◽  
Walter Tribett ◽  
Brian Bennett ◽  
...  
Keyword(s):  
Global Warming ◽  
Greenhouse Gases ◽  
Surface Temperature ◽  
Empirical Model ◽  
Global Climate ◽  
Volcanic Eruptions ◽  
Climate Record ◽  
Global Mean Surface Temperature ◽  
Global Mean ◽  
Human Component

<p>The Empirical Model of Global Climate (EM-GC) (Canty et al., ACP, 2013, McBride et al., ESDD, 2020) is a multiple linear regression, energy balance model that accounts for the natural influences on global mean surface temperature due to ENSO, the 11-year solar cycle, major volcanic eruptions, as well as the anthropogenic influence of greenhouse gases and aerosols and the efficiency of ocean heat uptake. First, we will analyze the human contribution of global warming from 1975-2014 based on the climate record, also known as the attributable anthropogenic warming rate (AAWR). We will compare the values of AAWR found using the EM-GC with values of AAWR from the CMIP6 multi-model ensemble. Preliminary analysis indicates that over the past three decades, the human component of global warming inferred from the CMIP6 GCMs is larger than the human component of warming from the climate record. Second, we will compare values of equilibrium climate sensitivity inferred from the historical climate record to those determined from CMIP6 GCMs using the Gregory et al., GRL, 2004 method. Third, we will use the future abundances of greenhouse gases and aerosols provided by the Shared Socioeconomic Pathways (SSPs) to project future global mean surface temperature change. We will compare the projections of future temperature anomalies from CMIP6 GCMs to those determined by the EM-GC. We will conclude by assessing the probability of the CMIP6 and EM-GC projections of achieving the Paris Agreement target (1.5°C) and upper limit (2.0°C) for several of the SSP scenarios.</p>

scholarly journals Atmospheric circulation and hydroclimate impacts of alternative warming scenarios for the Eocene

2017 ◽  
Vol 13 (8) ◽  
pp. 1037-1048 ◽  
Author(s):  
Henrik Carlson ◽  
Rodrigo Caballero
Keyword(s):  
Surface Temperature ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Cloud Albedo ◽  
Cloud Properties ◽  
Atmospheric Co2 Concentrations ◽  
Global Mean

Abstract. Recent work in modelling the warm climates of the early Eocene shows that it is possible to obtain a reasonable global match between model surface temperature and proxy reconstructions, but only by using extremely high atmospheric CO2 concentrations or more modest CO2 levels complemented by a reduction in global cloud albedo. Understanding the mix of radiative forcing that gave rise to Eocene warmth has important implications for constraining Earth's climate sensitivity, but progress in this direction is hampered by the lack of direct proxy constraints on cloud properties. Here, we explore the potential for distinguishing among different radiative forcing scenarios via their impact on regional climate changes. We do this by comparing climate model simulations of two end-member scenarios: one in which the climate is warmed entirely by CO2 (which we refer to as the greenhouse gas (GHG) scenario) and another in which it is warmed entirely by reduced cloud albedo (which we refer to as the low CO2–thin clouds or LCTC scenario) . The two simulations have an almost identical global-mean surface temperature and equator-to-pole temperature difference, but the LCTC scenario has  ∼  11 % greater global-mean precipitation than the GHG scenario. The LCTC scenario also has cooler midlatitude continents and warmer oceans than the GHG scenario and a tropical climate which is significantly more El Niño-like. Extremely high warm-season temperatures in the subtropics are mitigated in the LCTC scenario, while cool-season temperatures are lower at all latitudes. These changes appear large enough to motivate further, more detailed study using other climate models and a more realistic set of modelling assumptions.

scholarly journals Holocene global mean surface temperature, a multi-method reconstruction approach

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Darrell Kaufman ◽  
Nicholas McKay ◽  
Cody Routson ◽  
Michael Erb ◽  
Christoph Dätwyler ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Global Mean Surface Temperature ◽  
Global Mean

scholarly journals Comparing variability and trends in observed and modelled global-mean surface temperature

2010 ◽  
Vol 37 (16) ◽  
pp. n/a-n/a ◽  
Author(s):  
John C. Fyfe ◽  
Nathan P. Gillett ◽  
David W. J. Thompson
Keyword(s):  
Surface Temperature ◽  
Global Mean Surface Temperature ◽  
Global Mean
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